The non-specificity of affective symptoms between asthma and chronic obstructive pulmonary disease is a key challenge in medical practice. This is even more complicated when physicians fail to recognize the co-existence of these two diseases, a condition known as asthma-chronic obstructive pulmonary disease overlap. This occurrence mainly affects smokers, middle-aged and older age groups. Failure to diagnose a disease correctly and in a timely manner often leads to administration of wrong drug therapy, delayed treatment or wasted financial resources. To reduce the risk of misclassification, a differential diagnostic tool that can differentiate among patients with asthma, chronic obstructive pulmonary disease and asthma-chronic obstructive pulmonary disease overlap was developed based on measurements of spirometry, blood eosinophil count and smoking history. The study employed a judgmental sampling technique to draw 184 samples from the National Health and Nutrition Examination Survey (NHANES) database based on three classes of obstructive lung diseases. Rapid application development methodology was used as the software methodology upon which the architecture was designed and developed. A comparative analysis was made between a number of classification algorithms including K-nearest neighbour, support vector machines, logistic regression, multilayer perceptron and random forest. The results demonstrate that the differential diagnosic tool can correctly classify patients with obstructive lung diseases with an accuracy of 93.94%, showing an automated approach that would aid physicians in making preliminary diagnoses, leading to optimization of time resources, lower medical device costs and better patient outcomes. Further research regarding the tool’s improvement should focus on using a more robust dataset and evaluation in liaison with a physician in a real clinical setting.